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mcp-use is the open-source devtools and cloud infrastructure to help dev teams quickly build and deploy custom AI agents with MCP servers.
Our SDK has over 5,000 GitHub stars, 100k downloads, and is trusted by engineers at NASA, Cisco, NVIDIA, etc...
Dev teams use us at a variety of companies, and many others to build agentic products or internal custom agents.
🏰 mcp-use starts with a story
When MCP and the first use cases came out, we could not believe that such powerful tech could only be used on IDEs or Claude Desktop.
We felt the need to write agents ourselves, in code, in a structured and composable way, and allow other developers to do the same.
That’s why we first released mcp-use library. Now we want to make the development of MCP agents dev-friendly and production-ready.
🥁 Enter mcp-use Cloud Platform:
Dev teams can build application layer using our SDK which is deeply integrated with mcp-use Platform, the central control plane layer that acts like a gateway for all the MCP servers.
mcp-use provides a vertical solution for MCP development with the following offering:
mcp-use SDK: Easily integrate MCP-enabled AI agents into your product or internal tools.
mcp-use Cloud Platform: The central control plane layer for MCP servers, managing configs, server selection, caching, metrics, and access control.
mcp-use Server Hosting: managed/self-hosted servers, third-party MCP servers, and short-lived stdio sandboxed servers.
🚨 The problem we’re solving
We found that teams building AI agents frequently faced major friction points: agents need modular, plug-and-play integrations with diverse services, but most teams were hand-coding these integrations.
Without standardization, scaling agents became slow and error-prone.
Remote deployment and governance of MCP servers in enterprise settings remain unsolved issues. Also, it’s crucial to make MCP-enabled agents easily configurable, swappable, and loosely coupled.
Let's go through the main challenges that we found when talking with hundreds of developers in both startups and big enterprises:
Correctly build and deploy MCP servers
Fragmented MCP server configs
Handle auth, access control, and audit logging
Reduce the number of tools exposed
Manage environments and governance
Observability gap
Agents are mostly running locally
❓Why us
Our cloud platform provides developers with a single, unified interface for MCP. They can configure multiple MCP servers into a single pool, creating agents tailored to their applications. Developers integrate these agents through our SDK with just one line of code and embed them into their products. We handle all the hosting and deployment complexities.
Think of us as the Vercel and Next.js, but built for MCP development.
Try it out with personalized onboarding.
👉 Start here: https://mcp-use.com/
👉 Libraries: Python: https://github.com/mcp-use/mcp-use | Typescript: https://github.com/mcp-use/mcp-u...
We created mcp-use to be the vertical solution for MCP. What do you think?
P.S. Drop by the comments, we’d love your feedback! 👇
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